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Comparison of optimization techniques for modular neural networks applied to human recognition

Abstract:

In this paper a comparison of optimization techniques for a Modular Neural Network (MNN) with a granular approach is presented. A Hierarchical Genetic Algorithm, a Firefly Algorithm (FA), and a Grey Wolf Optimizer are developed to perform a comparison of results. These algorithms design optimal MNN architectures, where their main task is the optimization of some parameters of MNN such as, number of sub modules, percentage of information for the training phase and number of hidden layers (with their respective number of neurons) for each sub module and learning algorithm. The MNNs are applied to human recognition based on iris biometrics, where a benchmark database is used to perform the comparison, having as objective function in each optimization algorithm the minimization of the error of recognition.